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GraphPINE: Graph Importance propagation for interpretable drug response prediction.

Yoshitaka Inoue1,2, Tianfan Fu3, Augustin Luna2

  • 1Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, USA.

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|April 29, 2025
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Summary
This summary is machine-generated.

GraphPINE, a novel graph neural network (GNN), enhances explainability in biomedical research by integrating prior knowledge for drug response prediction. This approach improves feature learning and graph representation, outperforming existing methods.

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Area of Science:

  • Biomedical Informatics
  • Machine Learning
  • Computational Biology

Background:

  • Explainability is crucial in biomedical research, but current methods (attention, gradient, Shapley value) struggle with prior knowledge integration.
  • Existing techniques lack the ability to constrain explainability based on known relationships between predictive features.
  • A gap exists in methods that can leverage domain-specific prior knowledge to guide and constrain explainability in predictive models.

Purpose of the Study:

  • To introduce GraphPINE, a graph neural network (GNN) architecture designed to incorporate domain-specific prior knowledge for initializing and optimizing node importance.
  • To overcome the limitations of existing explainability methods by constraining results based on known biological relationships.
  • To enhance feature learning and graph representation in predictive tasks, specifically for drug response prediction.

Main Methods:

  • GraphPINE utilizes a graph neural network (GNN) architecture that leverages domain-specific prior knowledge to initialize node importance.
  • It incorporates an LSTM-like sequential format and an importance propagation layer for unified updates of feature matrices and node importance.
  • The model employs GNN-based graph propagation of feature values and is applied to cancer drug response prediction using gene-gene and drug-target interaction graphs.

Main Results:

  • GraphPINE achieved a Precision-Recall Area Under the Curve (PR-AUC) of 0.894 and a Receiver Operating Characteristic Area Under the Curve (ROC-AUC) of 0.796.
  • Performance was evaluated across 952 drugs using drug screening and gene data, incorporating over 5,000 gene nodes.
  • The model demonstrated effective integration of prior knowledge (gene-gene and drug-target interaction graphs) for improved prediction accuracy.

Conclusions:

  • GraphPINE offers a novel approach to explainability in biomedical research by effectively integrating prior knowledge into a GNN framework.
  • The architecture facilitates informed feature learning and improved graph representation, leading to enhanced predictive performance in drug response prediction.
  • This method addresses the limitations of current explainability techniques by providing constrained and knowledge-informed insights into predictive features.